Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation

Ari Peryanto; Anton Yudhana; Rusydi Umar

doi:10.30871/jaic.v4i1.2017

Ari Peryanto Universitas Ahmad Dahlan
Anton Yudhana Universitas Ahmad Dahlan
Rusydi Umar Universitas Ahmad Dahlan

DOI: https://doi.org/10.30871/jaic.v4i1.2017

Keywords: Convolution Neural Network, Image, Clasification, Cross Validation

Abstract

Image classification is a fairly easy task for humans, but for machines it is something that is very complex and is a major problem in the field of Computer Vision which has long been sought for a solution. There are many algorithms used for image classification, one of which is Convolutional Neural Network, which is the development of Multi Layer Perceptron (MLP) and is one of the algorithms of Deep Learning. This method has the most significant results in image recognition, because this method tries to imitate the image recognition system in the human visual cortex, so it has the ability to process image information. In this research the implementation of this method is done by using the Keras library with the Python programming language. The results showed the percentage of accuracy with K = 5 cross-validation obtained the highest level of accuracy of 80.36% and the highest average accuracy of 76.49%, and system accuracy of 72.02%. For the lowest accuracy obtained in the 4th and 5th testing with an accuracy value of 66.07%. The system that has been made has also been able to predict with the highest average prediction of 60.31%, and the highest prediction value of 65.47%.

Downloads

Download data is not yet available.

References

L. D. J. Le Cun , B. Boser , J. S. Denker , D. Henderson , R. E. Howard , W. Hubbard, “LeNet-5: Handwritten Digit Recognition with a Back-Propagation Network,” Dermatologic Surg., vol. 39, no. 1pt2, p. 149, 1998.

N. Sharma, V. Jain, and A. Mishra, “An Analysis of Convolutional Neural Networks for Image Classification,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 377–384, 2018.

W. S. Eka Putra, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101,” J. Tek. ITS, vol. 5, no. 1, 2016.

C. K. Dewa, A. L. Fadhilah, and A. Afiahayati, “Convolutional Neural Networks for Handwritten Javanese Character Recognition,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 12, no. 1, p. 83, 2018.

K. Chauhan and S. Ram, “International Journal of Advance Engineering and Research Image Classification with Deep Learning and Comparison between Different Convolutional Neural Network Structures using Tensorflow and Keras,” pp. 533–538, 2018.

A. Fadlil, R. Umar, and S. Gustina, “Mushroom Images Identification Using Orde 1 Statistics Feature Extraction with Artificial Neural Network Classification Technique Mushroom Images Identification Using Orde 1 Statistics Feature Extraction with Artificial Neural Network Classification Techn,” 2019.

S. Saifullah, S. Sunardi, and A. Yudhana, “Perbandingan Segmentasi Pada Citra Asli Dan Citra Kompresi Wavelet Untuk Identifikasi Telur,” Ilk. J. Ilm., vol. 8, no. 3, p. 190, 2016.

I. Riadi, R. Umar, and F. D. Aini, “Analisis Perbandingan Detection Traffic Anomaly Dengan Metode Naive Bayes Dan Support Vector Machine (Svm),” Ilk. J. Ilm., vol. 11, no. 1, p. 17, 2019.

H. Darmanto, D. Learning, T. Learning, and G. Descent, “PENGENALAN SPESIES IKAN BERDASARKAN KONTUR OTOLITH,” vol. 2, 2019.

S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” vol. 3, no. 2, pp. 49–56, 2018.

E. N. Arrofiqoh and H. Harintaka, “Implementasi Metode Convolutional Neural Network Untuk Klasifikasi Tanaman Pada Citra Resolusi Tinggi,” Geomatika, vol. 24, no. 2, p. 61, 2018.

D. C. K. Putu Aryasuta Wicaksana, I Made Sudarma, “Pengenalan Pola Motif Kain Tenun Gringsing Menggunakan Metode Convolutional Neural Network Dengan Model Arsitektur,” vol. 6, no. 3, pp. 159–168, 2019.

K. O’Shea and R. Nash, “An Introduction to Convolutional Neural Networks,” pp. 1–11, 2015.

B. J. Erickson, P. Korfiatis, Z. Akkus, T. Kline, and K. Philbrick, “Toolkits and Libraries for Deep Learning,” J. Digit. Imaging, vol. 30, no. 4, pp. 400–405, 2017.

C. Y. Lee, P. W. Gallagher, and Z. Tu, “Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree,” Proc. 19th Int. Conf. Artif. Intell. Stat. AISTATS 2016, pp. 464–472, 2016.

H. Abhirawan, Jondri, and A. Arifianto, “Pengenalan Wajah Menggunakan Convolutional Neural Networks (CNN),” Univ. Telkom, vol. 4, no. 3, pp. 4907–4916, 2017.

A. Santoso and G. Ariyanto, “Implementasi Deep Learning Berbasis Keras Untuk Pengenalan Wajah,” Emit. J. Tek. Elektro, vol. 18, no. 01, pp. 15–21, 2018.

C. Applications, “Mathematical and Computational Applications,” vol. 16, no. 3, pp. 702–711, 2011.

B. Hu, Z. Lu, H. Li, and Q. Chen, “Convolutional neural network architectures for matching natural language sentences,” Adv. Neural Inf. Process. Syst., vol. 3, no. January, pp. 2042–2050, 2014.

R. A. Surya, A. Fadlil, and A. Yudhana, “Ekstraksi Ciri Metode Gray Level Co-Occurrence Matrix ( GLCM ) dan Filter Gabor untuk Klasifikasi Citra Batik Pekalongan,” J. Inform. Pengemb. IT (JPIT , Vol. 02, No. 02, Juli 2017, vol. 02, no. 02, pp. 23–26, 2017.

S. Gustina et al., “Identifikasi Tanaman Kamboja menggunakan Ekstraksi Ciri Citra Daun dan Jaringan Syaraf Tiruan,” vol. 2, no. 1, pp. 128–132, 2016.

S. Saifullah, S. -, and A. Yudhana, “Analisis Perbandingan Pengolahan Citra Asli Dan Hasil Croping Untuk Identifikasi Telur,” J. Tek. Inform. dan Sist. Inf., vol. 2, no. 3, pp. 341–350, 2016.